Lake et al. [1]} proposed Omniglot benchmark to measure progress of generalization from few examples in various tasks such as classification, generation of new examples and parsing objects into parts. Omniglot dataset contains characters from 50 alphabets, 1623 character classes in total, each character represented by only 20 hand-drawn instances, available both in image and pen trajectory formats (a few samples are shown in Fig. REF ).
The dataset is split into a 30-alphabet Background set (with 964 classes) used for training, and a 20-alphabet Evaluation set (with 659 classes). Recently, the “Minimal” [2]} version of the training data has been proposed, containing two five-alphabet subsets of the Background set.
The evaluation procedure of the \(N\) -way one-shot classification task consists of multiple episodes. Each episode is a classification task with \(N\)  classes from the same alphabet. The classifier can use \(N\)  support examples (one example per class) to assign \(N\)  query examples to one of the \(N\)  classes. The Background set can be used for training. For evaluation, we use pre-defined episodes from the Evaluation set with \(N=20\) , which were proposed by the authors of Omniglot [1]}.
